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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python for Finance (2nd ed.)\n",
    "\n",
    "**Mastering Data-Driven Finance**\n",
    "\n",
    "&copy; Dr. Yves J. Hilpisch | The Python Quants GmbH\n",
    "\n",
    "<img src=\"http://hilpisch.com/images/py4fi_2nd_shadow.png\" width=\"300px\" align=\"left\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trading Strategies (a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Moving Averages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "from pylab import mpl, plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('seaborn')\n",
    "mpl.rcParams['font.family'] = 'serif'\n",
    "%config InlineBackend.figure_format = 'svg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw = pd.read_csv('http://hilpisch.com/tr_eikon_eod_data.csv',\n",
    "                  index_col=0, parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2216 entries, 2010-01-01 to 2018-06-29\n",
      "Data columns (total 12 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   AAPL.O  2138 non-null   float64\n",
      " 1   MSFT.O  2138 non-null   float64\n",
      " 2   INTC.O  2138 non-null   float64\n",
      " 3   AMZN.O  2138 non-null   float64\n",
      " 4   GS.N    2138 non-null   float64\n",
      " 5   SPY     2138 non-null   float64\n",
      " 6   .SPX    2138 non-null   float64\n",
      " 7   .VIX    2138 non-null   float64\n",
      " 8   EUR=    2216 non-null   float64\n",
      " 9   XAU=    2211 non-null   float64\n",
      " 10  GDX     2138 non-null   float64\n",
      " 11  GLD     2138 non-null   float64\n",
      "dtypes: float64(12)\n",
      "memory usage: 225.1 KB\n"
     ]
    }
   ],
   "source": [
    "raw.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "symbol = 'AAPL.O'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = (\n",
    "    pd.DataFrame(raw[symbol])\n",
    "    .dropna()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trading Strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "SMA1 = 42  \n",
    "SMA2 = 252  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['SMA1'] = data[symbol].rolling(SMA1).mean()  \n",
    "data['SMA2'] = data[symbol].rolling(SMA2).mean()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "</svg>\n"
      ],
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(figsize=(10, 6));\n",
    "# plt.savefig('../../images/ch15/strat_plot_01.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Position'] = np.where(data['SMA1'] > data['SMA2'], 1, -1)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL.O</th>\n",
       "      <th>SMA1</th>\n",
       "      <th>SMA2</th>\n",
       "      <th>Position</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-06-25</th>\n",
       "      <td>182.17</td>\n",
       "      <td>185.606190</td>\n",
       "      <td>168.265556</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-26</th>\n",
       "      <td>184.43</td>\n",
       "      <td>186.087381</td>\n",
       "      <td>168.418770</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-27</th>\n",
       "      <td>184.16</td>\n",
       "      <td>186.607381</td>\n",
       "      <td>168.579206</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-28</th>\n",
       "      <td>185.50</td>\n",
       "      <td>187.089286</td>\n",
       "      <td>168.736627</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29</th>\n",
       "      <td>185.11</td>\n",
       "      <td>187.470476</td>\n",
       "      <td>168.901032</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "            AAPL.O        SMA1        SMA2  Position\n",
       "Date                                                \n",
       "2018-06-25  182.17  185.606190  168.265556         1\n",
       "2018-06-26  184.43  186.087381  168.418770         1\n",
       "2018-06-27  184.16  186.607381  168.579206         1\n",
       "2018-06-28  185.50  187.089286  168.736627         1\n",
       "2018-06-29  185.11  187.470476  168.901032         1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "ax = data.plot(secondary_y='Position', figsize=(10, 6))\n",
    "ax.get_legend().set_bbox_to_anchor((0.25, 0.85));\n",
    "# plt.savefig('../../images/ch15/strat_plot_02.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorized Backtesting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Returns'] = np.log(data[symbol] / data[symbol].shift(1))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Strategy'] = data['Position'].shift(1) * data['Returns']  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <td>37.3223</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0081</td>\n",
       "      <td>0.0081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-06</th>\n",
       "      <td>47.6757</td>\n",
       "      <td>45.5226</td>\n",
       "      <td>37.3921</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.0008</td>\n",
       "      <td>-0.0008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             AAPL.O     SMA1     SMA2  Position  Returns  Strategy\n",
       "Date                                                              \n",
       "2010-12-31  46.0800  45.2810  37.1207         1      NaN       NaN\n",
       "2011-01-03  47.0814  45.3497  37.1862         1   0.0215    0.0215\n",
       "2011-01-04  47.3271  45.4126  37.2525         1   0.0052    0.0052\n",
       "2011-01-05  47.7142  45.4661  37.3223         1   0.0081    0.0081\n",
       "2011-01-06  47.6757  45.5226  37.3921         1  -0.0008   -0.0008"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.round(4).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Returns     4.017148\n",
       "Strategy    5.811299\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(data[['Returns', 'Strategy']].sum())  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Returns     0.250571\n",
       "Strategy    0.250407\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Returns', 'Strategy']].std() * 252 ** 0.5  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = data[['Returns', 'Strategy']].cumsum(\n",
    "        ).apply(np.exp).plot(figsize=(10, 6))\n",
    "data['Position'].plot(ax=ax, secondary_y='Position', style='--')\n",
    "ax.get_legend().set_bbox_to_anchor((0.25, 0.85));\n",
    "# plt.savefig('../../images/ch15/strat_plot_03.png');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "sma1 = range(20, 61, 4)  \n",
    "sma2 = range(180, 281, 10)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pd.DataFrame()\n",
    "for SMA1, SMA2 in product(sma1, sma2):  \n",
    "    data = pd.DataFrame(raw[symbol])\n",
    "    data.dropna(inplace=True)\n",
    "    data['Returns'] = np.log(data[symbol] / data[symbol].shift(1))\n",
    "    data['SMA1'] = data[symbol].rolling(SMA1).mean()\n",
    "    data['SMA2'] = data[symbol].rolling(SMA2).mean()\n",
    "    data.dropna(inplace=True)\n",
    "    data['Position'] = np.where(data['SMA1'] > data['SMA2'], 1, -1)\n",
    "    data['Strategy'] = data['Position'].shift(1) * data['Returns']\n",
    "    data.dropna(inplace=True)\n",
    "    perf = np.exp(data[['Returns', 'Strategy']].sum())\n",
    "    results = results.append(pd.DataFrame(\n",
    "                {'SMA1': SMA1, 'SMA2': SMA2,\n",
    "                 'MARKET': perf['Returns'],\n",
    "                 'STRATEGY': perf['Strategy'],\n",
    "                 'OUT': perf['Strategy'] - perf['Returns']},\n",
    "                 index=[0]), ignore_index=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 121 entries, 0 to 120\n",
      "Data columns (total 5 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   SMA1      121 non-null    int64  \n",
      " 1   SMA2      121 non-null    int64  \n",
      " 2   MARKET    121 non-null    float64\n",
      " 3   STRATEGY  121 non-null    float64\n",
      " 4   OUT       121 non-null    float64\n",
      "dtypes: float64(3), int64(2)\n",
      "memory usage: 4.9 KB\n"
     ]
    }
   ],
   "source": [
    "results.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "     SMA1  SMA2    MARKET  STRATEGY       OUT\n",
       "56     40   190  4.650342  7.175173  2.524831\n",
       "39     32   240  4.045619  6.558690  2.513071\n",
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "results.sort_values('OUT', ascending=False).head(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Walk Hypothesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "symbol = '.SPX'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(raw[symbol])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "lags = 5\n",
    "cols = []\n",
    "for lag in range(1, lags + 1):\n",
    "    col = 'lag_{}'.format(lag)  \n",
    "    data[col] = data[symbol].shift(lag)  \n",
    "    cols.append(col)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>.SPX</th>\n",
       "      <th>lag_1</th>\n",
       "      <th>lag_2</th>\n",
       "      <th>lag_3</th>\n",
       "      <th>lag_4</th>\n",
       "      <th>lag_5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>1132.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2010-01-05</th>\n",
       "      <td>1136.52</td>\n",
       "      <td>1132.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
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       "      <td>1136.52</td>\n",
       "      <td>1132.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2010-01-07</th>\n",
       "      <td>1141.69</td>\n",
       "      <td>1137.14</td>\n",
       "      <td>1136.52</td>\n",
       "      <td>1132.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>1144.98</td>\n",
       "      <td>1141.69</td>\n",
       "      <td>1137.14</td>\n",
       "      <td>1136.52</td>\n",
       "      <td>1132.99</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-11</th>\n",
       "      <td>1146.98</td>\n",
       "      <td>1144.98</td>\n",
       "      <td>1141.69</td>\n",
       "      <td>1137.14</td>\n",
       "      <td>1136.52</td>\n",
       "      <td>1132.99</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "               .SPX    lag_1    lag_2    lag_3    lag_4    lag_5\n",
       "Date                                                            \n",
       "2010-01-01      NaN      NaN      NaN      NaN      NaN      NaN\n",
       "2010-01-04  1132.99      NaN      NaN      NaN      NaN      NaN\n",
       "2010-01-05  1136.52  1132.99      NaN      NaN      NaN      NaN\n",
       "2010-01-06  1137.14  1136.52  1132.99      NaN      NaN      NaN\n",
       "2010-01-07  1141.69  1137.14  1136.52  1132.99      NaN      NaN\n",
       "2010-01-08  1144.98  1141.69  1137.14  1136.52  1132.99      NaN\n",
       "2010-01-11  1146.98  1144.98  1141.69  1137.14  1136.52  1132.99"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = np.linalg.lstsq(data[cols], data[symbol], rcond=-1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.966, 0.005, 0.025, 0.   , 0.003])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "reg.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
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   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(cols, reg);\n",
    "# plt.savefig('../../images/ch15/strat_plot_04.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Prediction'] = np.dot(data[cols], reg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
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       "<Figure size 720x432 with 1 Axes>"
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     "metadata": {},
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   ],
   "source": [
    "data[[symbol, 'Prediction']].iloc[-75:].plot(figsize=(10, 6));\n",
    "# plt.savefig('../../images/ch15/strat_plot_05.png');"
   ]
  },
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   "metadata": {},
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    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://tpq.io\" target=\"_blank\">http://tpq.io</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a> | <a href=\"mailto:training@tpq.io\">training@tpq.io</a>"
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